DETAILED ACTION
Information Disclosure Statement
The information disclosure statement submitted on 06/17/2025 has been considered by the Examiner and made of record in the application file.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 9-12 and 14-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by George (“An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification”).
Regarding claim 1, George discloses a computer-implemented method for classifying a subject into a cognitively normal (CN) condition, a mild cognitive impairment (MCI) condition and an Alzheimer's disease (AD) condition, the method comprising: (Four classifications are carried out using the ADNI data set, namely NC vs. AD, MCI vs. CN, AD vs. MCI, and AD vs. MCI vs. CN. For the three-class classification (AD vs. MCI vs. CN), TP, TN, FP, and FN are calculated for each class, see page 2108. For the three-class classification, AD vs. MCI vs. CN the accuracy obtained is 0.79, see page 2110.)
obtaining an image volume of the subject's brain; and (See figure 1, brain MRI scans. The proposed framework is comprised of two major stages: the pre-processing of raw MRI volume and the classification of pre-processed 3D data, see page 2101. A 3D CNN framework is disclosed for identifying multiple classes that take the whole image volume as input, see page 2103.)
using an AD Net model (3D CNN framework with attention mechanisms, see abstract. George proposes a 3D CNN framework for identifying multiple classes that take the whole image volume as input and uses both spatial and channel attention mechanisms, see page 2103.) to process the image volume to thereby generate a plurality of AD Net feature maps (The output of each convolution produces a feature map assembled along the depth to constitute the output volume and that there will be as many feature maps as there are filters, see page 2103. The feature map obtained from the 3D CNN is passed through an attention network to extract a more accurate feature representation of the input MRI volume, see page 2105.) and a first plurality of scores that respectively predict likelihoods of the CN, MCI and AD conditions, (The output layer or Softmax layer is the final layer and that the probabilities of the predicted classes are calculated using the Softmax activation function, and the class with the highest probability is considered to be the output, see page 2104. During forward propagation, the generated output indicates confidence in the predicted labels, and these probabilities are compared with the true labels, see page 2109. Since George discloses performing the three-class classification (AD vs. MCI vs. CN), the Softmax probabilities constitute the claimed first plurality of scores predicting likelihoods of the CN, MCI, and AD conditions.) wherein the AD Net model is an attention-enhanced convolutional neural network (CNN) (The pre-processed 3D MRI volume is then fed into a 3D CNN framework with attention mechanisms and that the proposed work used a mixed attention mechanism by combining channel and spatial attention modules, see page 2101. The framework uses both spatial and channel attention mechanisms, see page 2103.) formed by embedding an attention module into a CNN module. (The framework consists of two major building blocks, including the 3D convolution block and the attention (spatial and channel) block, see page 2103. The feature map obtained from the 3D CNN is passed through an attention network consisting of spatial and channel attention mechanisms before it is fed to the final predicting stage, see page 2104. The proposed framework employs sequential channel and spatial attention modules, see page 2105.)
Regarding claim 2, George discloses the claimed invention wherein classifying the subject into the CN, MCI and AD conditions according to the first plurality of scores. (pages 2104 and 2109)
Regarding claim 9, George discloses the claimed invention wherein the image volume is prepared from data obtained from three-dimensionally imaging the subject's brain by magnetic resonance imaging (MRI). (page 2101)
Regarding claim 10, George discloses the claimed invention wherein obtaining a raw-image volume of the subject's brain; and preprocessing the raw-image volume to generate the image volume such that the image volume is obtained. (page 2101)
Regarding claim 11, George discloses the claimed invention wherein the raw-image volume is pre-processed by performing linear registration, skull removal, bias field correction, and noise cutting and normalization. (page 2101)
Regarding claim 12, George discloses the claimed invention wherein training the AD Net model before the AD Net model is used to process the image volume. (pages 2108-2109)
Regarding claims 14 and 15, George discloses a computing system for classifying a subject into a cognitively normal (CN) condition, a mild cognitive impairment (MCI) condition and an Alzheimer's disease (AD) condition, the computing system comprising one or more computers configured to execute a process of classifying the subject into the CN, MCI and AD conditions according to the method of claims 1 and 2. (abstract)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over George in view of Ijaz (“Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning”).
Regarding claim 6, George discloses the claimed invention except for wherein the CNN module is realized as a Visual Geometry Group (VGG) model and the attention module is realized as a Convolutional Block Attention Module (CBAM).
In related art, Ijaz discloses the CNN module is realized as a Visual Geometry Group (VGG) model and the attention module is realized as a Convolutional Block Attention Module (CBAM). (abstract)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Ijaz into the teachings of George to effectively focus on relevant features.
Regarding claim 7, George, as modified by Ijaz, discloses the claimed invention wherein the VGG model is an optimized VGG 19 model. (abstract)
Allowable Subject Matter
Claims 3-5, 8, 13 and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/BOBBAK SAFAIPOUR/ Primary Examiner, Art Unit 2665
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